AI-based workflows
.jpeg)
ML-based workflows
We validate our ML workflows across independent data scientists with pathways in different programming languages.
- Assessing the best ML workflow
- Comparing decision tree analyses (Ensemble tools) with simpler regression models by integrating bootstrapping.
- Integration of image, clinical, geopositional or biomarker data
- Interpretation with our own and existing datasets
.jpeg)
Generative AI
Generative AI offers the capacity to map a virtually unlimited number of synthetic individuals with biomarker profiles that follow the distribution of biomarkers in real-life samples. There are over 10 different synthetic data generation workflows, each with distinct libraries in R and Python - which one is best for you?
- Synthetic clones
- Augmented data in model training
- Multi-context applicability and GAI workflow selection
.jpeg)
Predictive analysis
Utilising clinical, biochemical, molecular, socio-economic, geopositional and other relevant data to drive accurate prediction of future health.
- Study design
- Suitable AI/ML strategies
- Use of our tissue/data bank
- Predictive analyses and independent validation.
.jpeg)
Personalised medicine
Our AI-based workflows are designed to drive personalised decision making, cohort-based risk stratification as well as context (ethnicity/race)-driven predictive risk scores.
- Context-driven predictions
- Dynamic risk scores
- Static risk score assessment
- Integrated learning for personalised solutions to healthcare
All codes for workflows used/published by our group are available through Github/isletbiology/ or through collaborative arrangements and support by contacting Professor Hardikar.
.jpeg)
.jpeg)
.jpeg)
.jpeg)
.jpeg)
.jpeg)
.jpeg)